common_params: Template parameters

In InouyeLab/NetRep: Permutation Testing Network Module Preservation Across Datasets

Description

Template parameters to be imported into other function documentation. This is not intended to be a stand-alone help file.

Arguments

network	a list of interaction networks, one for each dataset. Each entry of the list should be a n * n matrix or where each element contains the edge weight between nodes i and j in the inferred network for that dataset.
data	a list of matrices, one for each dataset. Each entry of the list should be the data used to infer the interaction network for that dataset. The columns should correspond to variables in the data (nodes in the network) and rows to samples in that dataset.
correlation	a list of matrices, one for each dataset. Each entry of the list should be a n * n matrix where each element contains the correlation coefficient between nodes i and j in the data used to infer the interaction network for that dataset.
moduleAssignments	a list of vectors, one for each discovery dataset, containing the module assignments for each node in that dataset.
modules	a list of vectors, one for each discovery dataset, of modules to perform the analysis on. If unspecified, all modules in each discovery dataset will be analysed, with the exception of those specified in backgroundLabel argument.
backgroundLabel	a single label given to nodes that do not belong to any module in the moduleAssignments argument. Defaults to "0". Set to NULL if you do not want to skip the network background module.
discovery	a vector of names or indices denoting the discovery dataset(s) in the data, correlation, network, moduleAssignments, modules, and test lists.
test	a list of vectors, one for each discovery dataset, of names or indices denoting the test dataset(s) in the data, correlation, and network lists.
verbose	logical; should progress be reported? Default is TRUE.

InouyeLab/NetRep documentation built on Oct. 8, 2020, 2:32 a.m.